# ini merupakan cell untuk code
print("dan ini adalah cell code tempat menuliskan code python")

dan ini adalah cell code tempat menuliskan code python

angka = 10
angka

10

dss_name = "Data Science Series: Streamlining Invoice Processing"
print(dss_name)

Data Science Series: Streamlining Invoice Processing

## code here
dss_name == dss_name

True

# cek daftar keyword
import keyword
keyword.kwlist

['False',
 'None',
 'True',
 'and',
 'as',
 'assert',
 'async',
 'await',
 'break',
 'class',
 'continue',
 'def',
 'del',
 'elif',
 'else',
 'except',
 'finally',
 'for',
 'from',
 'global',
 'if',
 'import',
 'in',
 'is',
 'lambda',
 'nonlocal',
 'not',
 'or',
 'pass',
 'raise',
 'return',
 'try',
 'while',
 'with',
 'yield']

dss_name

'Data Science Series: Streamlining Invoice Processing'

# code here
type(dss_name)

str

# code here
nilai_ta = 100
nilai_fiqey = 90.0

nilai_ta - nilai_fiqey

10.0

2 + 2 == 5
# == --> untuk mengecek kondisi (apakah sama?)

False

# list berikut mengandung informasi nama, kota domisili, umur, apakah sudah menikah?
data_list = ['Sari', 'Jakarta', 27, False]

# cek tipe data my_list
type(data_list)

list

# akses informasi kota domilisi dari seorang Sari, dimana informasi kota domilisi terletak di urutan ke-2 (index ke-1)
data_list[1]

'Jakarta'

# coba kita tambahkan informasi pekerjaan 'Sari', yaitu 'Data Scientist'
data_list.append('Data Scientist')
data_list

['Sari', 'Jakarta', 27, False, 'Data Scientist']

# dictionary berisi informasi tentang seseorang
data_dict = {
    'nama': 'Sari',
    'kota_domisili': 'Jakarta',
    'umur': 27,
    'menikah': False
}

type(data_dict)

dict

data_dict['kota_domisili']

'Jakarta'

def nama_fungsi(parameter):
    perintah

# fungsi luas_segitiga
def luas_persegi(sisi):
    hasil = sisi * sisi
    return hasil

# memanggil fungsi
luas_persegi(sisi = 4)

16

jabodetabek = ['Jakarta', 'Bogor', 'Depok', 'Tangerang', 'Bekasi']

# for loop
for x in jabodetabek:
  print(x)

Jakarta
Bogor
Depok
Tangerang
Bekasi

import pandas as pd
print(pd.__version__)

2.1.4

pandas.read_csv("path/data")

receipts = pd.read_csv('data_input/receipt_cleaned.csv')
receipts

# code here
receipts.head()

# cek tipe data
receipts.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36 entries, 0 to 35
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   receipt_id   36 non-null     object 
 1   item_name    36 non-null     object 
 2   count        36 non-null     float64
 3   price        36 non-null     int64  
 4   total_price  36 non-null     int64  
dtypes: float64(1), int64(2), object(2)
memory usage: 1.5+ KB

df['column_name'] = df['column_name'].astype('new_data_types')

# check data type
receipts['item_name'] = receipts['item_name'].astype('category')

receipts.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36 entries, 0 to 35
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   receipt_id   36 non-null     category
 1   item_name    36 non-null     category
 2   count        36 non-null     float64 
 3   price        36 non-null     int64   
 4   total_price  36 non-null     int64   
dtypes: category(2), float64(1), int64(2)
memory usage: 3.1 KB

# mengubah semua teks dalam kolom 'item_name' menjadi huruf kecil
receipts['item_name'].str.lower()[5]

'm-carame 1 black tea'

# mengubah semua teks dalam kolom 'receipt_id' menjadi huruf besar
receipts['receipt_id'].str.upper()[5]

'ID0003'

# mengganti '-' dengan ' ' (spasi) dalam kolom 'item_name'
receipts['item_name'].str.replace('-', ' ')[5]

'm carame 1 black tea'

receipts['receipt_id'].value_counts()

receipt_id
ID0011    4
ID0000    3
ID0006    3
ID0019    3
ID0007    2
ID0008    2
ID0013    2
ID0014    2
ID0015    2
ID0018    2
ID0010    2
ID0004    1
ID0005    1
ID0003    1
ID0009    1
ID0001    1
ID0002    1
ID0012    1
ID0016    1
ID0017    1
Name: count, dtype: int64

# code here
receipts['item_name'].value_counts(ascending = False)

item_name
arem arem                       2
egg tart                        2
real ganache                    1
se'i s-pi lada hitam (j)        1
nasi putih                      1
milk shake coklat               1
es kopi susu                    1
mineral 600 ml                  1
bulgogi rice r                  1
kroket                          1
pepenero pastel                 1
tt                              1
lemonade 16oz                   1
beef c roll 3pcs                1
kaya bred                       1
futami 17 green tea (clas       1
se'i sapi sambal matah ( r )    1
choco peanut bread              1
serbu                           1
tahu ikan oma giok              1
mika kecil                      1
talam ungu                      1
cp 360 club card                1
choco devil                     1
white choco banana spread       1
oreo green tea spread           1
potato sausage bread            1
le mineral                      1
bbq chicken                     1
m-carame 1 black tea            1
s-ovaltine                      1
kopi susu kolonel               1
pizza toast                     1
grain croque monsieur           1
Name: count, dtype: int64

receipts[receipts['item_name'] == 'arem arem']

# code here
receipts[receipts['receipt_id'] == 'ID0015']

# code here
receipt_above50k = receipts[receipts['total_price'] > 50000]
receipt_above50k

# optional: mengambil yang unique saja
receipt_above50k['receipt_id'].unique()

['ID0006', 'ID0007', 'ID0010', 'ID0011', 'ID0012', 'ID0015']
Categories (20, object): ['ID0000', 'ID0001', 'ID0002', 'ID0003', ..., 'ID0016', 'ID0017', 'ID0018', 'ID0019']

import re
import os
import torch

from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel

c:\Users\user\miniconda3\envs\dss_donut\lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")

Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.
c:\Users\user\miniconda3\envs\dss_donut\lib\site-packages\torch\_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()

# set model device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

VisionEncoderDecoderModel(
  (encoder): DonutSwinModel(
    (embeddings): DonutSwinEmbeddings(
      (patch_embeddings): DonutSwinPatchEmbeddings(
        (projection): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))
      )
      (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
      (dropout): Dropout(p=0.0, inplace=False)
    )
    (encoder): DonutSwinEncoder(
      (layers): ModuleList(
        (0): DonutSwinStage(
          (blocks): ModuleList(
            (0-1): 2 x DonutSwinLayer(
              (layernorm_before): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
              (attention): DonutSwinAttention(
                (self): DonutSwinSelfAttention(
                  (query): Linear(in_features=128, out_features=128, bias=True)
                  (key): Linear(in_features=128, out_features=128, bias=True)
                  (value): Linear(in_features=128, out_features=128, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
                (output): DonutSwinSelfOutput(
                  (dense): Linear(in_features=128, out_features=128, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
              )
              (drop_path): DonutSwinDropPath(p=0.1)
              (layernorm_after): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
              (intermediate): DonutSwinIntermediate(
                (dense): Linear(in_features=128, out_features=512, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): DonutSwinOutput(
                (dense): Linear(in_features=512, out_features=128, bias=True)
                (dropout): Dropout(p=0.0, inplace=False)
              )
            )
          )
          (downsample): DonutSwinPatchMerging(
            (reduction): Linear(in_features=512, out_features=256, bias=False)
            (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
        )
        (1): DonutSwinStage(
          (blocks): ModuleList(
            (0-1): 2 x DonutSwinLayer(
              (layernorm_before): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
              (attention): DonutSwinAttention(
                (self): DonutSwinSelfAttention(
                  (query): Linear(in_features=256, out_features=256, bias=True)
                  (key): Linear(in_features=256, out_features=256, bias=True)
                  (value): Linear(in_features=256, out_features=256, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
                (output): DonutSwinSelfOutput(
                  (dense): Linear(in_features=256, out_features=256, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
              )
              (drop_path): DonutSwinDropPath(p=0.1)
              (layernorm_after): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
              (intermediate): DonutSwinIntermediate(
                (dense): Linear(in_features=256, out_features=1024, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): DonutSwinOutput(
                (dense): Linear(in_features=1024, out_features=256, bias=True)
                (dropout): Dropout(p=0.0, inplace=False)
              )
            )
          )
          (downsample): DonutSwinPatchMerging(
            (reduction): Linear(in_features=1024, out_features=512, bias=False)
            (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          )
        )
        (2): DonutSwinStage(
          (blocks): ModuleList(
            (0-13): 14 x DonutSwinLayer(
              (layernorm_before): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
              (attention): DonutSwinAttention(
                (self): DonutSwinSelfAttention(
                  (query): Linear(in_features=512, out_features=512, bias=True)
                  (key): Linear(in_features=512, out_features=512, bias=True)
                  (value): Linear(in_features=512, out_features=512, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
                (output): DonutSwinSelfOutput(
                  (dense): Linear(in_features=512, out_features=512, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
              )
              (drop_path): DonutSwinDropPath(p=0.1)
              (layernorm_after): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
              (intermediate): DonutSwinIntermediate(
                (dense): Linear(in_features=512, out_features=2048, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): DonutSwinOutput(
                (dense): Linear(in_features=2048, out_features=512, bias=True)
                (dropout): Dropout(p=0.0, inplace=False)
              )
            )
          )
          (downsample): DonutSwinPatchMerging(
            (reduction): Linear(in_features=2048, out_features=1024, bias=False)
            (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
          )
        )
        (3): DonutSwinStage(
          (blocks): ModuleList(
            (0-1): 2 x DonutSwinLayer(
              (layernorm_before): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (attention): DonutSwinAttention(
                (self): DonutSwinSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
                (output): DonutSwinSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.0, inplace=False)
                )
              )
              (drop_path): DonutSwinDropPath(p=0.1)
              (layernorm_after): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (intermediate): DonutSwinIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
                (intermediate_act_fn): GELUActivation()
              )
              (output): DonutSwinOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (dropout): Dropout(p=0.0, inplace=False)
              )
            )
          )
        )
      )
    )
    (pooler): AdaptiveAvgPool1d(output_size=1)
  )
  (decoder): MBartForCausalLM(
    (model): MBartDecoderWrapper(
      (decoder): MBartDecoder(
        (embed_tokens): Embedding(57580, 1024, padding_idx=1)
        (embed_positions): MBartLearnedPositionalEmbedding(770, 1024)
        (layers): ModuleList(
          (0-3): 4 x MBartDecoderLayer(
            (self_attn): MBartAttention(
              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
            )
            (activation_fn): GELUActivation()
            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (encoder_attn): MBartAttention(
              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
            )
            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (fc1): Linear(in_features=1024, out_features=4096, bias=True)
            (fc2): Linear(in_features=4096, out_features=1024, bias=True)
            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          )
        )
        (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
    )
    (lm_head): Linear(in_features=1024, out_features=57580, bias=False)
  )
)

# folder path
dir_path = 'data_input/valid/'

img_filenames = [] # list nama file gambar
images = [] # list objek gambar
labels = [] # list label dari gambar

# iterasi direktori
for file in os.listdir(dir_path):
    if file.endswith(('.png', '.jpg', '.jpeg')):
        # menyimpan nama invoice ke dalam list
        img_filenames.append(file)
        
        # membuka dan menyimpan objek gambar ke dalam list
        images.append(Image.open(dir_path + file))


    elif file.endswith('.txt'):
        # membuka file teks dan membaca isinya, lalu menyimpan ke dalam list
        with open(dir_path + file, 'r') as label_file: 
            labels.append(label_file.read())

# mencetak list nama file gambar
print(img_filenames)

['ID0000.png', 'ID0001.png', 'ID0002.png', 'ID0003.png', 'ID0004.png', 'ID0005.png', 'ID0006.png', 'ID0007.png', 'ID0008.png', 'ID0009.png', 'ID0010.png', 'ID0011.png', 'ID0012.png', 'ID0013.png', 'ID0014.png', 'ID0015.png', 'ID0016.png', 'ID0017.png', 'ID0018.png', 'ID0019.png', 'ID0020.png', 'ID0021.png', 'ID0022.png', 'ID0023.png', 'ID0024.png', 'ID0025.png', 'ID0026.png', 'ID0027.png', 'ID0028.png', 'ID0029.png', 'ID0030.png', 'ID0031.png', 'ID0032.png', 'ID0033.png', 'ID0034.png', 'ID0035.png', 'ID0036.png', 'ID0037.png', 'ID0038.png', 'ID0039.png', 'ID0040.png', 'ID0041.png', 'ID0042.png', 'ID0043.png', 'ID0044.png', 'ID0045.png', 'ID0046.png', 'ID0047.png', 'ID0048.png', 'ID0049.png', 'ID0050.png', 'ID0051.png', 'ID0052.png', 'ID0053.png', 'ID0054.png', 'ID0055.png', 'ID0056.png', 'ID0057.png', 'ID0058.png', 'ID0059.png', 'ID0060.png', 'ID0061.png', 'ID0062.png', 'ID0063.png', 'ID0064.png', 'ID0065.png', 'ID0066.png', 'ID0067.png', 'ID0068.png', 'ID0069.png', 'ID0070.png', 'ID0071.png', 'ID0072.png', 'ID0073.png', 'ID0074.png', 'ID0075.png', 'ID0076.png', 'ID0077.png', 'ID0078.png', 'ID0079.png', 'ID0080.png', 'ID0081.png', 'ID0082.png', 'ID0083.png', 'ID0084.png', 'ID0085.png', 'ID0086.png', 'ID0087.png', 'ID0088.png', 'ID0089.png', 'ID0090.png', 'ID0091.png', 'ID0092.png', 'ID0093.png', 'ID0094.png', 'ID0095.png', 'ID0096.png', 'ID0097.png', 'ID0098.png', 'ID0099.png']

# code here
print(labels[6])
images[6]

{'menu': [{'nm': 'POTATO SAUSAGE BREAD', 'cnt': '1', 'price': '19,000'}, {'nm': 'OREO GREEN TEA SPREAD', 'cnt': '1', 'price': '52,000'}, {'nm': 'WHITE CHOCO BANANA SPREAD', 'cnt': '1', 'price': '52,000'}], 'total': {'total_price': '123,000', 'creditcardprice': '123,000'}}

images[6].size

(864, 1296)

pixel_values = processor(images[6], return_tensors="pt").pixel_values
print(pixel_values.shape)

torch.Size([1, 3, 1280, 960])

task_prompt = "<s_cord-v2>"

processor.tokenizer.pad_token_id

1

outputs = model.generate(
        inputs=pixel_values.to(device),
        max_length=model.decoder.config.max_position_embeddings,
        decoder_input_ids=processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt")["input_ids"].to(device),
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
        use_cache=True,
        
        # modify parameters
        early_stopping=True,
        num_beams=2,
        output_scores=True,
    )

outputs.sequences

tensor([[57579, 57526, 57528, 40769, 44398, 54165, 13663, 56992, 50990, 42990,
         43547,  5909, 57527, 57530,  1314, 57529, 57532, 17888, 56239, 57531,
         57522, 57528, 37040, 49429, 42990, 51290,  4004, 42990,  4008,  5176,
         40099, 39415, 57527, 57530,  1314, 57529, 57532, 20017, 52066, 57531,
         57522, 57528, 46193, 53278, 33938, 36277, 18081, 56701, 42990, 40266,
          5178,  5176, 40099, 39415, 57527, 57530,  1314, 57529, 57532, 20017,
         52066, 57531, 57525, 57544, 57546, 40474, 47108, 57545, 57564, 40474,
         47108, 57563, 57543,     2]])

raw_sequence = processor.batch_decode(outputs.sequences)[0]
print(raw_sequence)

<s_cord-v2><s_menu><s_nm> POTATO SAUSAGE BREAD</s_nm><s_cnt> 1</s_cnt><s_price> 19,000</s_price><sep/><s_nm> OREO GREEN TEA SPREAD</s_nm><s_cnt> 1</s_cnt><s_price> 52,000</s_price><sep/><s_nm> WHITE CHOCO BANANA SPREAD</s_nm><s_cnt> 1</s_cnt><s_price> 52,000</s_price></s_menu><s_total><s_total_price> 123,000</s_total_price><s_creditcardprice> 123,000</s_creditcardprice></s_total></s>

sequence = raw_sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "") # remove </s> eos and <pad> token
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove <s_cord-v2> first task start token
sequence

'<s_menu><s_nm> POTATO SAUSAGE BREAD</s_nm><s_cnt> 1</s_cnt><s_price> 19,000</s_price><sep/><s_nm> OREO GREEN TEA SPREAD</s_nm><s_cnt> 1</s_cnt><s_price> 52,000</s_price><sep/><s_nm> WHITE CHOCO BANANA SPREAD</s_nm><s_cnt> 1</s_cnt><s_price> 52,000</s_price></s_menu><s_total><s_total_price> 123,000</s_total_price><s_creditcardprice> 123,000</s_creditcardprice></s_total>'

processor.token2json(sequence)

{'menu': [{'nm': 'POTATO SAUSAGE BREAD', 'cnt': '1', 'price': '19,000'},
  {'nm': 'OREO GREEN TEA SPREAD', 'cnt': '1', 'price': '52,000'},
  {'nm': 'WHITE CHOCO BANANA SPREAD', 'cnt': '1', 'price': '52,000'}],
 'total': {'total_price': '123,000', 'creditcardprice': '123,000'}}

# gambar yang di generate (images[6])
images[6]

def doc_to_text(input_img, task_prompt=task_prompt, model=model, processor=processor):
    # set model device
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)
    
    # document preprocessing
    pixel_values = processor(input_img, return_tensors="pt").pixel_values
    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]

    # sequence generation
    outputs = model.generate(
        pixel_values.to(device),
        decoder_input_ids=decoder_input_ids.to(device),
        max_length=model.decoder.config.max_position_embeddings,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        use_cache=True,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
        
        # modify parameters
        early_stopping=True,
        num_beams=2,
        output_scores=True,
    )
    
    # document post-processing: sequence token cleaning
    sequence = processor.batch_decode(outputs.sequences)[0]
    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
    sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
    print(sequence)

    # output conversion: token to json
    output = processor.token2json(sequence)

    return output

# list untuk menyimpan hasil generate docs to text
preds = []

for img in images[:20]:
    out = doc_to_text(img)
    preds.append(out)

<s_menu><s_nm> REAL GANACHE</s_nm><s_cnt> 1</s_cnt><s_price> 16,500</s_price><sep/><s_nm> EGG TART</s_nm><s_cnt> 1</s_cnt><s_price> 13,000</s_price><sep/><s_nm> PIZZA TOAST</s_nm><s_cnt> 1</s_cnt><s_price> 16,000</s_price></s_menu><s_total><s_total_price> 45,500</s_total_price><s_cashprice> 50,000</s_cashprice><s_changeprice> 4,500</s_changeprice></s_total>
<s_menu><s_nm> Kopi Susu Kolonel</s_nm><s_cnt> 1</s_cnt><s_price> 23.000</s_price></s_menu><s_total><s_total_price> 23.000</s_total_price><s_cashprice> 50.000</s_cashprice><s_changeprice> 27.000</s_changeprice></s_total>
<s_menu><s_nm> S-Ovaltine</s_nm><s_unitprice> 20,000</s_unitprice><s_cnt> 1</s_cnt><s_price> 20,000</s_price><s_sub><s_nm> 50%</s_nm></s_sub></s_menu><s_sub_total><s_subtotal_price> 18,181</s_subtotal_price><s_tax_price> 1,818</s_tax_price></s_sub_total><s_total><s_total_price> 20,000</s_total_price><s_cashprice> 100,000</s_cashprice><s_changeprice> 80,000</s_changeprice></s_total>
<s_menu><s_nm> M-Carame 1 Black Tea</s_nm><s_unitprice> @28,000</s_unitprice><s_cnt> 1X</s_cnt><s_price> 28,000</s_price><s_sub><s_nm> 70%</s_nm><sep/><s_nm> Less Ice</s_nm></s_sub></s_menu><s_sub_total><s_subtotal_price> 28,000</s_subtotal_price><s_tax_price> 0</s_tax_price></s_sub_total><s_total><s_total_price> 28,000</s_total_price><s_cashprice> 28,000</s_cashprice><s_changeprice> 0</s_changeprice></s_total>
<s_menu><s_nm> BBQ Chicken</s_nm><s_cnt> 1</s_cnt><s_price> 41,000</s_price><s_sub><s_nm> - Sedang</s_nm><s_cnt> 1</s_cnt><s_price> 0</s_price></s_sub></s_menu><s_sub_total><s_subtotal_price> 41,000</s_subtotal_price></s_sub_total><s_total><s_total_price> 41,000</s_total_price><s_cashprice> 50.000</s_cashprice><s_changeprice> 9,000</s_changeprice><s_menuqty_cnt> 1</s_menuqty_cnt></s_total>
<s_menu><s_nm> LE MINERAL</s_nm><s_price> 8,000</s_price></s_menu><s_sub_total><s_subtotal_price> 7,273</s_subtotal_price><s_tax_price> 727</s_tax_price></s_sub_total><s_total><s_total_price> 8,000</s_total_price><s_cashprice> 8,000</s_cashprice></s_total>
<s_menu><s_nm> POTATO SAUSAGE BREAD</s_nm><s_cnt> 1</s_cnt><s_price> 19,000</s_price><sep/><s_nm> OREO GREEN TEA SPREAD</s_nm><s_cnt> 1</s_cnt><s_price> 52,000</s_price><sep/><s_nm> WHITE CHOCO BANANA SPREAD</s_nm><s_cnt> 1</s_cnt><s_price> 52,000</s_price></s_menu><s_total><s_total_price> 123,000</s_total_price><s_creditcardprice> 123,000</s_creditcardprice></s_total>
<s_menu><s_nm> Choco Devil</s_nm><s_cnt> 4</s_cnt><s_price> 63,636</s_price><sep/><s_nm> CP 360 Club Card</s_nm><s_price> -9,545</s_price></s_menu><s_sub_total><s_subtotal_price> 63,636</s_subtotal_price><s_discount_price> -9,545</s_discount_price><s_tax_price> 5,409</s_tax_price></s_sub_total><s_total><s_total_price> 59,500</s_total_price><s_cashprice> 100,000</s_cashprice><s_changeprice> 40,500</s_changeprice></s_total>
<s_menu><s_nm> TALAM UNGU</s_nm><s_unitprice> @6500</s_unitprice><s_cnt> 3X</s_cnt><s_discountprice> -7,800</s_discountprice><s_price> 19,500</s_price><sep/><s_nm> MIKA KECIL</s_nm><s_unitprice> @0</s_unitprice><s_cnt> 1X</s_cnt><s_price> 0</s_price></s_menu><s_sub_total><s_subtotal_price> 11,700</s_subtotal_price></s_sub_total><s_total><s_total_price> 11,700</s_total_price><s_cashprice> 20,000</s_cashprice><s_changeprice> 8,300</s_changeprice><s_menuqty_cnt> 4.00xITEMs</s_menuqty_cnt></s_total>
<s_menu><s_nm> Tahu Ikan Oma Giok</s_nm><s_cnt> 1</s_cnt><s_price> 20.000</s_price></s_menu><s_total><s_total_price> 20.000</s_total_price><s_cashprice> 20.000</s_cashprice><s_changeprice> 0</s_changeprice></s_total>
<s_menu><s_nm> Serbu</s_nm><s_cnt> 2</s_cnt><s_price> 40.000</s_price><sep/><s_nm> Choco Peanut Bread</s_nm><s_cnt> 2</s_cnt><s_price> 20.000</s_price></s_menu><s_total><s_total_price> 60.000</s_total_price><s_cashprice> 60.000</s_cashprice><s_changeprice> 0</s_changeprice></s_total>
<s_menu><s_nm> Se'I Sapi Sambal Matah ( R )</s_nm><s_cnt> 1</s_cnt><s_price> 20.000</s_price><sep/><s_nm> Se'I S-pi Lada Hitam (J)</s_nm><s_cnt> 1</s_cnt><s_price> 35.000</s_price><sep/><s_nm> Nasi Putih</s_nm><s_cnt> 2</s_cnt><s_price> 10.000</s_price><sep/><s_nm> Milk Shake Coklat</s_nm><s_cnt> 1</s_cnt><s_price> 16.000</s_price></s_menu><s_sub_total><s_subtotal_price> 81.000</s_subtotal_price><s_tax_price> 8.100</s_tax_price></s_sub_total><s_total><s_total_price> 89.100</s_total_price><s_changeprice> 0</s_changeprice><s_creditcardprice> 89.100</s_creditcardprice></s_total>
<s_menu><s_nm> ES KOPI SUSU</s_nm><s_cnt> 4</s_cnt><s_price> 72.000</s_price></s_menu><s_total><s_total_price> 72.000</s_total_price><s_changeprice> 0</s_changeprice><s_emoneyprice> 72.000</s_emoneyprice></s_total>
<s_menu><s_nm> MINERAL 600 ML</s_nm><s_cnt> 1</s_cnt><s_price> 7,727</s_price><sep/><s_nm> BULGOGI RICE R</s_nm><s_cnt> 1</s_cnt><s_price> 33,636</s_price></s_menu><s_sub_total><s_subtotal_price> 41,364</s_subtotal_price><s_tax_price> 4,136</s_tax_price></s_sub_total><s_total><s_total_price> 45,500</s_total_price><s_cashprice> 50,000</s_cashprice><s_changeprice> -4,500</s_changeprice></s_total>
<s_menu><s_nm> Arem Arem</s_nm><s_unitprice> @12.000</s_unitprice><s_cnt> 2x</s_cnt><s_price> 24.000</s_price><sep/><s_nm> Kroket</s_nm><s_unitprice> @12.000</s_unitprice><s_cnt> 1x</s_cnt><s_price> 12.000</s_price></s_menu><s_sub_total><s_subtotal_price> Rp 36.000</s_subtotal_price><s_tax_price> Rp 3.600</s_tax_price></s_sub_total><s_total><s_total_price> Rp 39.600</s_total_price><s_emoneyprice> Rp 39.600</s_emoneyprice></s_total>
<s_menu><s_nm> Arem Arem</s_nm><s_unitprice> 12.000</s_unitprice><s_cnt> 2</s_cnt><s_price> 24.000</s_price><sep/><s_nm> Pepenero Pastel</s_nm><s_unitprice> 15.000</s_unitprice><s_cnt> 2</s_cnt><s_price> 30.000</s_price></s_menu><s_sub_total><s_subtotal_price> Rp 54.000</s_subtotal_price><s_tax_price> Rp 5.400</s_tax_price></s_sub_total><s_total><s_total_price> Rp 59.400</s_total_price><s_cashprice> Rp 100.000</s_cashprice><s_changeprice> Rp 40.600</s_changeprice></s_total>
<s_menu><s_nm> TT</s_nm><s_unitprice> 20,000</s_unitprice><s_cnt> 1</s_cnt><s_price> 20,000</s_price></s_menu><s_total><s_total_price> 20,000</s_total_price><s_cashprice> 100,000</s_cashprice><s_changeprice> 80,000</s_changeprice></s_total>
<s_menu><s_nm> LEMONADE 16OZ</s_nm><s_unitprice> 20,000</s_unitprice><s_cnt> 1 x</s_cnt><s_price> 20,000</s_price></s_menu><s_sub_total><s_subtotal_price> 20,000</s_subtotal_price></s_sub_total><s_total><s_total_price> 20,000</s_total_price><s_cashprice> 100,000</s_cashprice><s_changeprice> 80,000</s_changeprice></s_total>
<s_menu><s_nm> beef c roll 3pcs</s_nm><s_unitprice> 10,000</s_unitprice><s_cnt> 1</s_cnt><s_price> 10,000</s_price><sep/><s_nm> kaya bred</s_nm><s_unitprice> 15,000</s_unitprice><s_cnt> 1</s_cnt><s_price> 15,000</s_price></s_menu><s_total><s_total_price> 25,000</s_total_price><s_cashprice> 100,000</s_cashprice><s_changeprice> 75,000</s_changeprice><s_menuqty_cnt> 2</s_menuqty_cnt></s_total>
<s_menu><s_nm> FUTAMI 17 GREEN TEA (CLAS</s_nm><s_cnt> 1</s_cnt><s_price> 12,500</s_price><sep/><s_nm> EGG TART</s_nm><s_cnt> 1</s_cnt><s_price> 13,000</s_price><sep/><s_nm> GRAIN CROQUE MONSIEUR</s_nm><s_cnt> 1</s_cnt><s_price> 17,000</s_price></s_menu><s_total><s_total_price> 42,500</s_total_price><s_cashprice> 50,000</s_cashprice><s_changeprice> 7,500</s_changeprice></s_total>

preds[0]

{'menu': [{'nm': 'REAL GANACHE', 'cnt': '1', 'price': '16,500'},
  {'nm': 'EGG TART', 'cnt': '1', 'price': '13,000'},
  {'nm': 'PIZZA TOAST', 'cnt': '1', 'price': '16,000'}],
 'total': {'total_price': '45,500',
  'cashprice': '50,000',
  'changeprice': '4,500'}}

df_preds = pd.DataFrame.from_dict(preds)
df_preds.head()

df_preds.isna().sum()

menu         0
total        0
sub_total    9
dtype: int64

df = pd.DataFrame(columns=['receipt_id', 'nm', 'cnt', 'price', 'total_price'])
df

# cara mengecek jumlah baris pada suatu dataframe
len(df)

0

# mengiterasi setiap rrow dalam df_preds
for index, row in df_preds.iterrows():

    # mengecek apakah field 'menu' adalah list, jika tidak, ubah menjadi list
    menus = row['menu'] if isinstance(row['menu'], list) else [row['menu']]

    # mengiterasi setiap elemen dalam list 'menus'
    for menu in menus:
        # menambahkan row baru ke 'df' dengan informasi dari 'menu' serta tambahan 'total_price' dan 'receipt_id'
        df.loc[len(df)] = {
            **menu,  # unpack semua pasangan key-value dari dictionary 'menu' yang cocok dg nama kolom yg ada
            'total_price': row['total']['total_price'],  # mengambil 'total_price' dari baris saat ini
            'receipt_id': img_filenames[index].split('.')[0]  # mengambil ID struk dari nama file gambar
        }

df

# menyimpan DataFrame 'df' ke dalam file csv
df.to_csv('receipt_extracted.csv', 
          index=False)  # berarti indeks DataFrame tidak disertakan dalam file csv

# cek tipe data
df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 36 entries, 0 to 35
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   receipt_id   36 non-null     object
 1   nm           36 non-null     object
 2   cnt          34 non-null     object
 3   price        36 non-null     object
 4   total_price  36 non-null     object
dtypes: object(5)
memory usage: 1.7+ KB

new_columns_name = {
    'nm': 'item_name',
    'cnt': 'quantity'
}

df = df.rename(columns=new_columns_name)
df.head()

# fungsi untuk membersihkan string harga dari simbol dan spasi
def clean_price(x):
    return int(x.replace(".", "").replace(",", "").replace("Rp", "").replace(" ", ""))

clean_price(df.loc[0, 'price'])

16500

# mengaplikasikan fungsi 'clean_price' ke setiap elemen di kolom 'price' pada 'df'
df['price'] = df['price'].apply(lambda x: clean_price(str(x)))

# mengaplikasikan fungsi 'clean_price' ke setiap elemen di kolom 'total_price' pada 'df'
df['total_price'] = df['total_price'].apply(lambda x: clean_price(x))

# membersihkan dan mengkonversi nilai 'x' menjadi float
def clean_quantity(x):
    # jika 'x' bernilai None (tidak ada data), kembalikan 0
    if x is None:
        return 0

    # mengonversi 'x' ke string, menghapus spasi, 'x'/'X', dan ']' dari string tersebut, dan mengembalikan menjadi sebuah angka
    return float(str(x).replace(" ", "").replace("x", "").replace("X", "").replace("]", ""))

# mengaplikasikan fungsi 'clean_quantity' ke setiap elemen di kolom 'quantity' pada 'df'
df['quantity'] = df['quantity'].apply(lambda x: clean_quantity(x))

# code here
df_pivot = df.pivot_table(index=['receipt_id', 'total_price', 'item_name'])
df_pivot

import gradio as gr

c:\Users\user\miniconda3\envs\dss_donut\lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

gr.Interface(input=, output=, fn=, live=)

import gradio as gr

def my_function(name_text):
    return f"Hello, {name_text}. Nice to meet you!"

test = gr.Interface(fn=my_function, inputs="text", outputs="text", live=True, title="Hello World App")
test.launch()

Running on local URL:  http://127.0.0.1:7861

To create a public link, set `share=True` in `launch()`.

model.eval()

demo = gr.Interface(
    fn=doc_to_text,
    inputs="image",
    outputs="json",
    title=f"🗃️ Streamlining Invoice Processing: Document Understanding Transformer",
     description="Extract text from receipts and invoices instantly.",
)
demo.launch(debug=True, share=True)

import re
import os
import torch

import gradio as gr
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel

processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")

Pandas dtype	Python type	Usage
object, str	str	Text or mixed numeric and non-numeric values
int64	int	Integer numbers
float64	float	Floating point numbers
bool	bool	True/False values
datetime64[ns]	-	Date and time values
timedelta[ns]	-	Differences between two datetimes
category	-	Finite list of text values

	menu	total	sub_total
0	[{'nm': 'REAL GANACHE', 'cnt': '1', 'price': '...	{'total_price': '45,500', 'cashprice': '50,000...	NaN
1	{'nm': 'Kopi Susu Kolonel', 'cnt': '1', 'price...	{'total_price': '23.000', 'cashprice': '50.000...	NaN
2	{'nm': 'S-Ovaltine', 'unitprice': '20,000', 'c...	{'total_price': '20,000', 'cashprice': '100,00...	{'subtotal_price': '18,181', 'tax_price': '1,8...
3	{'nm': 'M-Carame 1 Black Tea', 'unitprice': '@...	{'total_price': '28,000', 'cashprice': '28,000...	{'subtotal_price': '28,000', 'tax_price': '0'}
4	{'nm': 'BBQ Chicken', 'cnt': '1', 'price': '41...	{'total_price': '41,000', 'cashprice': '50.000...	{'subtotal_price': '41,000'}

	receipt_id	item_name	count	price	total_price
0	ID0000	real ganache	1.0	16500	45500
1	ID0000	egg tart	1.0	13000	45500
2	ID0000	pizza toast	1.0	16000	45500
3	ID0001	kopi susu kolonel	1.0	23000	23000
4	ID0002	s-ovaltine	1.0	20000	20000
5	ID0003	m-carame 1 black tea	1.0	28000	28000
6	ID0004	bbq chicken	1.0	41000	41000
7	ID0005	le mineral	1.0	8000	8000
8	ID0006	potato sausage bread	1.0	19000	123000
9	ID0006	oreo green tea spread	1.0	52000	123000
10	ID0006	white choco banana spread	1.0	52000	123000
11	ID0007	choco devil	4.0	63636	59500
12	ID0007	cp 360 club card	1.0	-9545	59500
13	ID0008	talam ungu	3.0	19500	11700
14	ID0008	mika kecil	1.0	0	11700
15	ID0009	tahu ikan oma giok	1.0	20000	20000
16	ID0010	serbu	2.0	40000	60000
17	ID0010	choco peanut bread	2.0	20000	60000
18	ID0011	se'i sapi sambal matah ( r )	1.0	20000	89100
19	ID0011	se'i s-pi lada hitam (j)	1.0	35000	89100
20	ID0011	nasi putih	2.0	10000	89100
21	ID0011	milk shake coklat	1.0	16000	89100
22	ID0012	es kopi susu	4.0	72000	72000
23	ID0013	mineral 600 ml	1.0	7727	45500
24	ID0013	bulgogi rice r	1.0	33636	45500
25	ID0014	arem arem	2.0	24000	39600
26	ID0014	kroket	1.0	12000	39600
27	ID0015	arem arem	2.0	24000	59400
28	ID0015	pepenero pastel	2.0	30000	59400
29	ID0016	tt	1.0	20000	20000
30	ID0017	lemonade 16oz	1.0	20000	20000
31	ID0018	beef c roll 3pcs	1.0	10000	25000
32	ID0018	kaya bred	1.0	15000	25000
33	ID0019	futami 17 green tea (clas	1.0	12500	42500
34	ID0019	egg tart	1.0	13000	42500
35	ID0019	grain croque monsieur	1.0	17000	42500

	receipt_id	nm	cnt	price	total_price
0	ID0000	REAL GANACHE	1	16,500	45,500
1	ID0000	EGG TART	1	13,000	45,500
2	ID0000	PIZZA TOAST	1	16,000	45,500
3	ID0001	Kopi Susu Kolonel	1	23.000	23.000
4	ID0002	S-Ovaltine	1	20,000	20,000
5	ID0003	M-Carame 1 Black Tea	1X	28,000	28,000
6	ID0004	BBQ Chicken	1	41,000	41,000
7	ID0005	LE MINERAL	NaN	8,000	8,000
8	ID0006	POTATO SAUSAGE BREAD	1	19,000	123,000
9	ID0006	OREO GREEN TEA SPREAD	1	52,000	123,000
10	ID0006	WHITE CHOCO BANANA SPREAD	1	52,000	123,000
11	ID0007	Choco Devil	4	63,636	59,500
12	ID0007	CP 360 Club Card	NaN	-9,545	59,500
13	ID0008	TALAM UNGU	3X	19,500	11,700
14	ID0008	MIKA KECIL	1X	0	11,700
15	ID0009	Tahu Ikan Oma Giok	1	20.000	20.000
16	ID0010	Serbu	2	40.000	60.000
17	ID0010	Choco Peanut Bread	2	20.000	60.000
18	ID0011	Se'I Sapi Sambal Matah ( R )	1	20.000	89.100
19	ID0011	Se'I S-pi Lada Hitam (J)	1	35.000	89.100
20	ID0011	Nasi Putih	2	10.000	89.100
21	ID0011	Milk Shake Coklat	1	16.000	89.100
22	ID0012	ES KOPI SUSU	4	72.000	72.000
23	ID0013	MINERAL 600 ML	1	7,727	45,500
24	ID0013	BULGOGI RICE R	1	33,636	45,500
25	ID0014	Arem Arem	2x	24.000	Rp 39.600
26	ID0014	Kroket	1x	12.000	Rp 39.600
27	ID0015	Arem Arem	2	24.000	Rp 59.400
28	ID0015	Pepenero Pastel	2	30.000	Rp 59.400
29	ID0016	TT	1	20,000	20,000
30	ID0017	LEMONADE 16OZ	1 x	20,000	20,000
31	ID0018	beef c roll 3pcs	1	10,000	25,000
32	ID0018	kaya bred	1	15,000	25,000
33	ID0019	FUTAMI 17 GREEN TEA (CLAS	1	12,500	42,500
34	ID0019	EGG TART	1	13,000	42,500
35	ID0019	GRAIN CROQUE MONSIEUR	1	17,000	42,500

			price	quantity
receipt_id	total_price	item_name
ID0000	45500	EGG TART	13000.0	1.0
		PIZZA TOAST	16000.0	1.0
		REAL GANACHE	16500.0	1.0
ID0001	23000	Kopi Susu Kolonel	23000.0	1.0
ID0002	20000	S-Ovaltine	20000.0	1.0
ID0003	28000	M-Carame 1 Black Tea	28000.0	1.0
ID0004	41000	BBQ Chicken	41000.0	1.0
ID0005	8000	LE MINERAL	8000.0	NaN
ID0006	123000	OREO GREEN TEA SPREAD	52000.0	1.0
		POTATO SAUSAGE BREAD	19000.0	1.0
		WHITE CHOCO BANANA SPREAD	52000.0	1.0
ID0007	59500	CP 360 Club Card	-9545.0	NaN
ID0007	59500	Choco Devil	63636.0	4.0
ID0008	11700	MIKA KECIL	0.0	1.0
ID0008	11700	TALAM UNGU	19500.0	3.0
ID0009	20000	Tahu Ikan Oma Giok	20000.0	1.0
ID0010	60000	Choco Peanut Bread	20000.0	2.0
ID0010	60000	Serbu	40000.0	2.0
ID0011	89100	Milk Shake Coklat	16000.0	1.0
		Nasi Putih	10000.0	2.0
		Se'I S-pi Lada Hitam (J)	35000.0	1.0
		Se'I Sapi Sambal Matah ( R )	20000.0	1.0
ID0012	72000	ES KOPI SUSU	72000.0	4.0
ID0013	45500	BULGOGI RICE R	33636.0	1.0
ID0013	45500	MINERAL 600 ML	7727.0	1.0
ID0014	39600	Arem Arem	24000.0	2.0
ID0014	39600	Kroket	12000.0	1.0
ID0015	59400	Arem Arem	24000.0	2.0
ID0015	59400	Pepenero Pastel	30000.0	2.0
ID0016	20000	TT	20000.0	1.0
ID0017	20000	LEMONADE 16OZ	20000.0	1.0
ID0018	25000	beef c roll 3pcs	10000.0	1.0
ID0018	25000	kaya bred	15000.0	1.0
ID0019	42500	EGG TART	13000.0	1.0
		FUTAMI 17 GREEN TEA (CLAS	12500.0	1.0
		GRAIN CROQUE MONSIEUR	17000.0	1.0

DSS: Streamlining Invoice Processing¶

Document Understanding Transformer for Efficient Business Operations in Python¶

Background¶

Output¶

🎯 Training Objectives¶

Python Programming Basics¶

Anaconda Environment Preparation¶

Cara Membuat Environtment Baru:¶

Install/Update Package¶

🧪 Knowledge Check¶

Working with Jupyter Notebook (.ipynb)¶

Markdown Cell dan Code Cell¶

Command Mode and Edit Mode¶

Understanding Fundamental of Variables & Python Data Types¶

Variables¶

Python Data Types¶

(1) 🔠 String¶

(2) 🔢 Number¶

(3) ✅ Boolean¶

(4) 🗃️ List¶

(5) 🧾Dictionary¶

📌 Highlight Poin: Tipe Data di Python¶

Exploring Python Functions and Looping Concepts¶

Function¶

Looping (Perulangan)¶

Working with Dataframe pandas¶

Understanding Dataframe pandas¶

Read Data¶

pandas Data Types¶

Additional: Object & Categorical Variables¶

Master Data Manipulation & Pre-processing¶

Dive Deeper into String Data Types¶

Contingency Tables / Frequency Tables¶

Perform Conditional Subsetting Operations¶

Document Understanding Transformer (DUT) Utilization¶

Introduction to 🍩 Donut Model¶

What is 🍩 Donut Model?¶

Why Use 🍩 Donut Model?¶

How to Use 🍩 Donut Model for Document Information Extraction?¶

Document Information Extraction Using Donut¶

1️⃣ Read Document Images¶

2️⃣ Document Pre-processing¶

3️⃣ Sequence Generation¶

4️⃣ Document Post-Processing: Sequence Token Cleaning¶

5️⃣ Output Conversion: Token to JSON¶

🍩 All-in-One Function to Generate Document Information¶

Data Analysis Enhancement with Donut Outputs¶

Transform Donut Outputs to Dataframe pandas¶

💭 Discussion¶

Data Wrangling¶

Data Cleaning¶

🤿 (Opsional) Dive Deeper: 🍩 Advanced Document Extracted Dataframe¶

Integration Gradio for Efficient Scanning¶

Understand the Fundamentals of Gradio¶

Main Concept:¶

Additional gr.Interface Parameters:¶

Simple Examples of Usage¶

Develop Gradio for Real-time Receipt and Invoice Scanning¶

(Opsional): Deploy the Gradio¶

Reference¶

[Additional] 🛠️ Tools Verification¶

Working with Dataframe `pandas`¶

Understanding Dataframe `pandas`¶

`pandas` Data Types¶

Transform Donut Outputs to Dataframe `pandas`¶

Additional `gr.Interface` Parameters:¶